Configure CShelve from a Python dictionary

Configure from a Python dictionary

open_from_dict() lets you pass configuration directly as a Python dictionary instead of a .ini file. This is ideal when you generate settings at runtime, pull secrets from a vault, or run in environments where writing to disk is inconvenient.

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Why use a dict?

• No temp files – Great for serverless or read-only filesystems.
• Dynamic config – Build settings from environment variables or secret managers.
• Same structure – Mirrors the .ini format, so examples are easy to translate.

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Quick start

import os
import cshelve

config = {
    "default": {
        "provider": "azure-blob",
        "auth_type": "passwordless",
        "account_url": "https://<account>.blob.core.windows.net"
        "container_name": "standard"
    },
}

with cshelve.open_from_dict(config) as db:
    db["hello"] = "world"
    print(db["hello"])

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API

cshelve.open_from_dict(config: dict, *args, **kwargs)

• config – A dictionary mirroring the .ini structure (sections + key/value pairs).
• *args, **kwargs – Same flags and options supported by cshelve.open().

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Provider examples

Azure Blob (passwordless)

config = {
    "default": {
        "provider": "azure-blob",
        "auth_type": "passwordless",
        "account_url": "https://myaccount.blob.core.windows.net",
        "container_name": "mycontainer",
    }
}

with cshelve.open_from_dict(config) as db:
    db["key"] = "value"

Azure Blob (connection string)

import os

config = {
    "default": {
        "provider": "azure-blob",
        "auth_type": "connection_string",
        "environment_key": "AZURE_STORAGE_CONNECTION_STRING",
        "container_name": "standard",
    },
    "logging": {"http": True, "credentials": False, "level": "INFO"},
}

os.environ["AZURE_STORAGE_CONNECTION_STRING"] = "<your-conn-string>"

with cshelve.open_from_dict(config) as db:
    db["config_type"] = "dict"

AWS S3 (access key)

import os

config = {
    "default": {
        "provider": "aws-s3",
        "auth_type": "access_key",
        "bucket_name": "mybucket",
        "key_id": "$AWS_KEY_ID",
        "key_secret": "$AWS_KEY_SECRET",
    }
}

os.environ["AWS_KEY_ID"] = "AKIA...snip..."
os.environ["AWS_KEY_SECRET"] = "secret...snip..."

with cshelve.open_from_dict(config) as db:
    db["cloud_key"] = "Stored in S3!"

SFTP (on-prem / self-hosted)

import os

config = {
    "default": {
        "provider": "sftp",
        "hostname": "$SFTP_PASSWORD_HOSTNAME",
        "username": "$SFTP_USERNAME",
        "password": "$SFTP_PASSWORD",
        "auth_type": "password",
    },
    "provider_params": {
        "remote_path": "myuser"
    }
}

os.environ.update({
    "SFTP_PASSWORD_HOSTNAME": "sftp.example.com",
    "SFTP_USERNAME": "myuser",
    "SFTP_PASSWORD": "mypassword",
})

with cshelve.open_from_dict(config) as db:
    db["local_backup"] = "Stored via SFTP"

Multi-Provider with Provider-Specific Compression

Store data to both local filesystem and AWS S3 simultaneously, with different compression levels:

import os

config = {
    "default": {
        "providers": "filesystem, s3",
        "provider_routing": "all",
    },
    "filesystem": {
        "provider": "filesystem",
        "folder_path": "/data/local",
    },
    "filesystem.compression": {
        "algorithm": "zlib",
        "level": "6",
    },
    "s3": {
        "provider": "aws-s3",
        "bucket_name": "my-backup-bucket",
        "auth_type": "access_key",
        "key_id": "$AWS_ACCESS_KEY_ID",
        "key_secret": "$AWS_SECRET_ACCESS_KEY",
    },
    "s3.compression": {
        "algorithm": "zlib",
        "level": "9",
    },
}

os.environ.update({
    "AWS_ACCESS_KEY_ID": "AKIA...snip...",
    "AWS_SECRET_ACCESS_KEY": "secret...snip...",
})

with cshelve.open_from_dict(config) as db:
    # Written to both filesystem (level 6) and S3 (level 9)
    db["backup_data"] = "Replicated to local and cloud"

    # Read from first provider (filesystem) - fast access
    print(db["backup_data"])

In this example:

• Filesystem uses compression level 6 (moderate – faster I/O)
• AWS S3 uses compression level 9 (maximum – cheaper bandwidth)
• All writes are replicated to both providers
• Reads only use the filesystem (first provider)